def find_tls(params,
pdb_hierarchy,
xray_structure,
return_as_list=False,
ignore_pdb_header_groups=False,
out=None):
"""
!!! WARNING! incoming xray_structure here gets converted to
isotropic B-factors IN PLACE.
"""
if (out is None):
out = sys.stdout
print_statistics.make_header("Analyzing inputs", out=out)
if (params.random_seed is None):
params.random_seed = flex.get_random_seed()
random.seed(params.random_seed)
flex.set_random_seed(params.random_seed)
xray_structure.convert_to_isotropic()
sites_cart = xray_structure.sites_cart()
u_cart = None
u_iso = xray_structure.extract_u_iso_or_u_equiv() #*adptbx.u_as_b(1.) # ?
bad_i_seqs = check_adp(u_iso=u_iso, out=out)
if (bad_i_seqs is not None):
atoms = pdb_hierarchy.atoms()
bad_atom_strings = []
for i_seq in bad_i_seqs[:10]:
atom_str = atoms[i_seq].format_atom_record()
bad_atom_strings.append(atom_str)
if (len(bad_i_seqs) > 10):
bad_atom_strings.append("... (remaining %d not shown)" %
(len(bad_i_seqs) - 10))
raise Sorry(
("%d atoms in the model contain isotropic B-factors <= 0:\n" +
"\n".join(bad_atom_strings)) % (len(bad_i_seqs)))
#
ssm = mmtbx.secondary_structure.manager(pdb_hierarchy=pdb_hierarchy,
sec_str_from_pdb_file=None,
params=None,
log=out)
alpha_h_selection = ssm.helix_selection()
secondary_structure_selection = ssm.helix_selection() | \
ssm.beta_selection() | ssm.base_pair_selection()
if (u_cart is not None):
assert secondary_structure_selection.size() == u_cart.size()
else:
assert secondary_structure_selection.size() == u_iso.size()
ssm.show_summary(log=out)
chains_and_residue_selections, secondary_structure_selection = chains_and_atoms(
pdb_hierarchy=pdb_hierarchy,
secondary_structure_selection=secondary_structure_selection,
out=out)
chains_and_permutations = []
chains_and_atom_selection_strings = []
print_statistics.make_header("Processing chains", out=out)
if (params.nproc is None):
params.nproc = 1
for crs in chains_and_residue_selections:
print_statistics.make_sub_header("Processing chain '%s'" % crs[0],
out=out)
chain_selection = chain_selection_from_residues(crs[1])
groups, perms = get_model_partitioning(
residues=crs[1],
secondary_structure_selection=secondary_structure_selection,
out=out)
#
if (len(perms) == 1):
print(" Whole chain is considered as one TLS group.", file=out)
chains_and_atom_selection_strings.append([
crs[0],
permutations_as_atom_selection_string(groups, perms[0])
])
else:
print(" Fitting TLS matrices...", file=out)
dic = {}
target_best = 1.e+9
if (params.nproc is Auto) or (params.nproc > 1):
process_perms = analyze_permutations(groups=groups,
sites_cart=sites_cart,
u_cart=u_cart,
u_iso=u_iso)
from libtbx import easy_mp
stdout_and_targets = easy_mp.pool_map(
processes=params.nproc,
fixed_func=process_perms,
args=perms,
chunksize=100,
func_wrapper="buffer_stdout_stderr")
targets = [t for so, t in stdout_and_targets]
for (perm, target) in zip(perms, targets):
dic.setdefault(len(perm), []).append([target, perm])
else:
for i_perm, perm in enumerate(perms):
if i_perm % 500 == 0:
print(" ...perm %d of %d" % (i_perm, len(perms)),
file=out)
selections = tls_group_selections(groups, perm)
target = 0
for selection in selections:
mo = tls_refinery(u_cart=u_cart,
u_iso=u_iso,
sites_cart=sites_cart,
selection=selection)
target += mo.f
dic.setdefault(len(perm), []).append([target, perm])
#print " perm %d of %d: target=%8.3f (TLS groups: %s), permutation:"%(
# i_perm, len(perms),target,len(perm)),perm
print(" Best fits:", file=out)
print(" No. of Targets", file=out)
print(" groups best rand.pick diff. score permutation",
file=out)
score_best = -1.e+9
perm_choice = None
for k, v in six.iteritems(dic):
t_best = v[0][0]
perm_best = v[0][1]
for v_ in v:
if (v_[0] < t_best):
t_best = v_[0]
perm_best = v_[1]
if (u_cart is not None):
u_cart_ = u_cart.select(chain_selection)
else:
u_cart_ = None
if (u_iso is not None):
u_iso_ = u_iso.select(chain_selection)
else:
u_iso_ = None
r = tls_refinery_random_groups(
u_cart=u_cart_,
u_iso=u_iso_,
sites_cart=sites_cart.select(chain_selection),
n_groups=k)
score = (r - t_best) / (r + t_best) * 100.
print(" %3d %6.3f %6.3f %6.2f %6.3f" %
(k, t_best, r, r - t_best, score),
perm_best,
file=out)
if (score > score_best):
score_best = score
perm_choice = perm_best[:]
#
chains_and_permutations.append([crs[0], perm_choice])
chains_and_atom_selection_strings.append([
crs[0],
permutations_as_atom_selection_string(groups, perm_choice)
])
#
print_statistics.make_header("SUMMARY", out=out)
#print "Optimal TLS groups:"
#for chain_and_permutation in chains_and_permutations:
# print chain_and_permutation
#print
print("TLS atom selections for phenix.refine:", file=out)
groups_out = StringIO()
selection_strings = []
print("refinement.refine.adp {", file=groups_out)
for r in chains_and_atom_selection_strings:
prefix = "chain '%s'" % r[0]
if (len(r[1]) > 0 and len(r[1:]) > 0):
prefix += " and "
for r_ in r[1:]:
for r__ in r_:
if (len(r__) > 0):
group_selection = prefix + "(%s)" % r__
print(" tls = \"%s\"" % group_selection,
file=groups_out)
selection_strings.append("%s" % group_selection)
else:
print(" tls = \"%s\"" % prefix, file=groups_out)
selection_strings.append("%s" % prefix)
print("}", file=groups_out)
print(groups_out.getvalue(), file=out)
print(file=out)
#XXX
if 0:
merge_groups_by_connectivity(pdb_hierarchy=pdb_hierarchy,
xray_structure=xray_structure,
selection_strings=selection_strings)
#XXX
if (len(selection_strings) > 0):
total_target = total_score(pdb_hierarchy=pdb_hierarchy,
sites_cart=sites_cart,
u_iso=u_iso,
selection_strings=selection_strings)
print(
"Overall best total target for automatically found groups: %10.1f"
% total_target,
file=out)
print(file=out)
if (return_as_list):
return selection_strings
else:
return groups_out.getvalue()
def manage_random_seed(random_seed):
if (random_seed is None):
random_seed = flex.get_random_seed()
random.seed(random_seed)
flex.set_random_seed(random_seed)
def find_tls (params,
pdb_inp,
pdb_hierarchy,
xray_structure,
return_as_list=False,
ignore_pdb_header_groups=False,
out=None) :
if (out is None) :
out = sys.stdout
print_statistics.make_header("Analyzing inputs", out=out)
if (params.random_seed is None) :
params.random_seed = flex.get_random_seed()
random.seed(params.random_seed)
flex.set_random_seed(params.random_seed)
xray_structure.convert_to_isotropic()
sites_cart = xray_structure.sites_cart()
u_cart = None
u_iso = xray_structure.extract_u_iso_or_u_equiv()#*adptbx.u_as_b(1.) # ?
bad_i_seqs = check_adp(u_iso=u_iso, out=out)
if (bad_i_seqs is not None) :
atoms = pdb_hierarchy.atoms()
bad_atom_strings = []
for i_seq in bad_i_seqs[:10] :
atom_str = atoms[i_seq].format_atom_record()
bad_atom_strings.append(atom_str)
if (len(bad_i_seqs) > 10) :
bad_atom_strings.append("... (remaining %d not shown)" %
(len(bad_i_seqs)-10))
raise Sorry(("%d atoms in the model contain isotropic B-factors <= 0:\n"+
"\n".join(bad_atom_strings)) % (len(bad_i_seqs)))
#
ssm = mmtbx.secondary_structure.manager(
pdb_hierarchy = pdb_hierarchy,
sec_str_from_pdb_file = None,
params = None)
alpha_h_selection = ssm.alpha_selection()
secondary_structure_selection = ssm.alpha_selection() | \
ssm.beta_selection() | ssm.base_pair_selection()
if(u_cart is not None):
assert secondary_structure_selection.size() == u_cart.size()
else:
assert secondary_structure_selection.size() == u_iso.size()
ssm.show_summary(log=out)
chains_and_residue_selections, secondary_structure_selection = chains_and_atoms(
pdb_hierarchy = pdb_hierarchy,
secondary_structure_selection = secondary_structure_selection,
out = out)
chains_and_permutations = []
chains_and_atom_selection_strings = []
print_statistics.make_header("Processing chains", out=out)
if (params.nproc is None) :
params.nproc = 1
for crs in chains_and_residue_selections:
print_statistics.make_sub_header("Processing chain '%s'"%crs[0],
out=out)
chain_selection = chain_selection_from_residues(crs[1])
groups, perms = get_model_partitioning(residues = crs[1],
secondary_structure_selection = secondary_structure_selection,
out = out)
#
#print
#selection_arrays = sels_as_selection_arrays(sels = groups)
#merge_groups_by_connectivity(
# pdb_hierarchy = pdb_hierarchy,
# xray_structure = xray_structure,
# selection_arrays = selection_arrays)
#assert 0
#
if(len(perms)==1):
print >> out, " Whole chain is considered as one TLS group."
chains_and_atom_selection_strings.append([crs[0],
permutations_as_atom_selection_string(groups, perms[0])])
else:
print >> out, " Fitting TLS matrices..."
dic = {}
target_best = 1.e+9
if (params.nproc is Auto) or (params.nproc > 1) :
process_perms = analyze_permutations(
groups=groups,
sites_cart=sites_cart,
u_cart=u_cart,
u_iso=u_iso)
from libtbx import easy_mp
stdout_and_targets = easy_mp.pool_map(
processes=params.nproc,
fixed_func=process_perms,
args=perms,
chunksize=100,
func_wrapper="buffer_stdout_stderr")
targets = [ t for so, t in stdout_and_targets ]
for (perm, target) in zip(perms, targets) :
dic.setdefault(len(perm), []).append([target,perm])
else :
for i_perm, perm in enumerate(perms):
if i_perm%500==0:
print >> out, " ...perm %d of %d"%(i_perm, len(perms))
selections = tls_group_selections(groups, perm)
target = 0
for selection in selections:
mo = tls_refinery(
u_cart = u_cart,
u_iso = u_iso,
sites_cart = sites_cart,
selection = selection)
target += mo.f
dic.setdefault(len(perm), []).append([target,perm])
#print " perm %d of %d: target=%8.3f (TLS groups: %s), permutation:"%(
# i_perm, len(perms),target,len(perm)),perm
print >> out, " Best fits:"
print >> out, " No. of Targets"
print >> out, " groups best rand.pick diff. score permutation"
score_best = -1.e+9
perm_choice = None
for k, v in zip(dic.keys(),dic.values()):
t_best = v[0][0]
perm_best = v[0][1]
for v_ in v:
if(v_[0]<t_best):
t_best = v_[0]
perm_best = v_[1]
if(u_cart is not None):
u_cart_ = u_cart.select(chain_selection)
else: u_cart_ = None
if(u_iso is not None):
u_iso_ = u_iso.select(chain_selection)
else: u_iso_ = None
r = tls_refinery_random_groups(
u_cart = u_cart_,
u_iso = u_iso_,
sites_cart = sites_cart.select(chain_selection),
n_groups = k)
score = (r-t_best)/(r+t_best)*100.
print >> out, " %3d %6.3f %6.3f %6.2f %6.3f"%(
k,t_best, r, r-t_best, score), perm_best
if(score > score_best):
score_best = score
perm_choice = perm_best[:]
#
chains_and_permutations.append([crs[0],perm_choice])
chains_and_atom_selection_strings.append([crs[0],
permutations_as_atom_selection_string(groups, perm_choice)])
#
if (pdb_inp is not None) and (not ignore_pdb_header_groups) :
external_tls_selections = external_tls(
pdb_inp = pdb_inp,
pdb_hierarchy = pdb_hierarchy,
sites_cart = sites_cart,
u_iso = u_iso,
out = out)
print_statistics.make_header("SUMMARY", out=out)
#print "Optimal TLS groups:"
#for chain_and_permutation in chains_and_permutations:
# print chain_and_permutation
#print
print >> out, "TLS atom selections for phenix.refine:"
groups_out = cStringIO.StringIO()
selection_strings = []
print >> groups_out, "refinement.refine.adp {"
for r in chains_and_atom_selection_strings:
prefix = "chain '%s'"%r[0]
if(len(r[1])>0 and len(r[1:])>0):
prefix += " and "
for r_ in r[1:]:
for r__ in r_:
if(len(r__)>0):
group_selection = prefix+"(%s)"%r__
print >> groups_out, " tls = \"%s\"" % group_selection
selection_strings.append("%s" % group_selection)
else:
print >> groups_out, " tls = \"%s\"" % prefix
selection_strings.append("%s" % prefix)
print >> groups_out, "}"
print >> out, groups_out.getvalue()
print >> out
#XXX
if 0:
merge_groups_by_connectivity(
pdb_hierarchy = pdb_hierarchy,
xray_structure = xray_structure,
selection_strings = selection_strings)
#XXX
if(len(selection_strings)>0):
total_target = total_score(
pdb_hierarchy = pdb_hierarchy,
sites_cart = sites_cart,
u_iso = u_iso,
selection_strings = selection_strings)
print >> out, "Overall best total target for automatically found groups: %10.1f"%total_target
print >> out
if (return_as_list) :
return selection_strings
else :
return groups_out.getvalue()
def manage_random_seed(random_seed):
if(random_seed is None):
random_seed = flex.get_random_seed()
random.seed(random_seed)
flex.set_random_seed(random_seed)